Trajectories and Comparative Analysis of Global Countries Dominating AI Publications, 2000-2025

[2509.25298] Trajectories and Comparative Analysis of Global Countries Dominating AI Publications, 2000-2025 Skip to main content Search Submit Donate Log in Computer Science > Computers and Society arXiv:2509.25298 (cs) [Submitted on 29 Sep 2025 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Trajectories and Comparative Analysis of Global Countries Dominating AI Publications, 2000-2025 Authors: Jason Hung View PDF HTML (experimental) Abstract: This study investigates the shifting global dynamics of Artificial Intelligence (AI) research by analysing the trajectories of countries dominating AI publications between 2000 and 2025. Drawing on the comprehensive OpenAlex datasets and employing fractional counting to avoid double attribution in co-authored work, the research maps the relative shares of AI publications across major global players. The analysis reveals a profound restructuring of the international AI research landscape. The US and the European Union (representing EU27), once the undisputed and established leaders, have experienced a notable decline in relative dominance, with their combined share of publications falling from over 57% in 2000 to less than 25% in 2025. In contrast, China has undergone a dramatic ascent, expanding its global share of AI publications from under 5% in 2000 to nearly 36% by 2025, therefore emerging as the single most dominant contributor. Alongside China, India has also risen substantially, consolidating a multipolar Asian research ecosystem. These empirical findings highlight the strategic implications of concentrated research output, particularly China's capacity to shape the future direction of AI innovation and standard-setting. Beyond publication volume, the study further examines research quality by comparing each country's share of high-impact publications against its overall output, and analyses citation impact trajectories across major players. The findings show that in addition to China leading in volume, the country has also recently led in high-impact publications. Such an observation challenges the general assumption that Western powers retain dominance in high-impact AI scholarship. Comments: 23 pages, 12 figures, 7 tables Subjects: Computers and Society (cs.CY) ; Digital Libraries (cs.DL) Cite as: arXiv:2509.25298 [cs.CY] (or arXiv:2509.25298v3 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2509.25298 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jason Hung [ view email ] [v1] Mon, 29 Sep 2025 16:35:54 UTC (630 KB) [v2] Sat, 14 Mar 2026 21:03:00 UTC (1,667 KB) [v3] Tue, 25 Aug 2026 07:06:49 UTC (1,655 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CY < prev | next > new | recent | 2025-09 Change to browse by: cs cs.DL References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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